






'''simple docstring'''





from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.layoutlm.modeling_tf_layoutlm import (
						    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLayoutLMForMaskedLM,
						    TFLayoutLMForQuestionAnswering,
						    TFLayoutLMForSequenceClassification,
						    TFLayoutLMForTokenClassification,
						    TFLayoutLMModel,
						)


class  snake_case__      :




					def __init__(  self  :		Union[str, Any]				, _A  :		Optional[Any]				, _A  :		Union[str, Any]=13				, _A  :		int=7				, _A  :		Union[str, Any]=True				, _A  :		Tuple=True				, _A  :		Any=True				, _A  :		List[str]=True				, _A  :		int=99				, _A  :		Any=32				, _A  :		int=2				, _A  :		int=4				, _A  :		int=37				, _A  :		Optional[Any]="gelu"				, _A  :		str=0.1				, _A  :		List[Any]=0.1				, _A  :		int=5_12				, _A  :		List[str]=16				, _A  :		List[str]=2				, _A  :		int=0.02				, _A  :		str=3				, _A  :		Dict=4				, _A  :		Tuple=None				, _A  :		str=10_00				, )    ->							Tuple:
								UpperCAmelCase_      :       Dict          =       parent
								UpperCAmelCase_      :       List[str]          =       batch_size
								UpperCAmelCase_      :       str          =       seq_length
								UpperCAmelCase_      :       Optional[Any]          =       is_training
								UpperCAmelCase_      :       str          =       use_input_mask
								UpperCAmelCase_      :       Union[str, Any]          =       use_token_type_ids
								UpperCAmelCase_      :       Any          =       use_labels
								UpperCAmelCase_      :       str          =       vocab_size
								UpperCAmelCase_      :       int          =       hidden_size
								UpperCAmelCase_      :       int          =       num_hidden_layers
								UpperCAmelCase_      :       Optional[Any]          =       num_attention_heads
								UpperCAmelCase_      :       List[str]          =       intermediate_size
								UpperCAmelCase_      :       int          =       hidden_act
								UpperCAmelCase_      :       int          =       hidden_dropout_prob
								UpperCAmelCase_      :       Dict          =       attention_probs_dropout_prob
								UpperCAmelCase_      :       Dict          =       max_position_embeddings
								UpperCAmelCase_      :       str          =       type_vocab_size
								UpperCAmelCase_      :       str          =       type_sequence_label_size
								UpperCAmelCase_      :       List[Any]          =       initializer_range
								UpperCAmelCase_      :       List[Any]          =       num_labels
								UpperCAmelCase_      :       Optional[Any]          =       num_choices
								UpperCAmelCase_      :       Dict          =       scope
								UpperCAmelCase_      :       Union[str, Any]          =       range_bbox




					def 		A      (  self  :		Tuple      )    ->							int:
								UpperCAmelCase_      :       Optional[int]          =       ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size      )

								# convert bbox to numpy since TF does not support item assignment
								UpperCAmelCase_      :       Optional[int]          =       ids_tensor([self.batch_size, self.seq_length, 4]				, self.range_bbox      ).numpy()
								# Ensure that bbox is legal
								for i in range(bbox.shape[0]      ):
											for j in range(bbox.shape[1]      ):
														if bbox[i, j, 3] < bbox[i, j, 1]:
																	UpperCAmelCase_      :       Any          =       bbox[i, j, 3]
																	UpperCAmelCase_      :       Dict          =       bbox[i, j, 1]
																	UpperCAmelCase_      :       Any          =       t
														if bbox[i, j, 2] < bbox[i, j, 0]:
																	UpperCAmelCase_      :       Union[str, Any]          =       bbox[i, j, 2]
																	UpperCAmelCase_      :       Dict          =       bbox[i, j, 0]
																	UpperCAmelCase_      :       List[Any]          =       t
								UpperCAmelCase_      :       int          =       tf.convert_to_tensor(_A      )

								UpperCAmelCase_      :       Optional[Any]          =       None
								if self.use_input_mask:
											UpperCAmelCase_      :       Optional[Any]          =       random_attention_mask([self.batch_size, self.seq_length]      )

								UpperCAmelCase_      :       int          =       None
								if self.use_token_type_ids:
											UpperCAmelCase_      :       Dict          =       ids_tensor([self.batch_size, self.seq_length]				, self.type_vocab_size      )

								UpperCAmelCase_      :       Dict          =       None
								UpperCAmelCase_      :       List[Any]          =       None
								UpperCAmelCase_      :       List[str]          =       None
								if self.use_labels:
											UpperCAmelCase_      :       int          =       ids_tensor([self.batch_size]				, self.type_sequence_label_size      )
											UpperCAmelCase_      :       Optional[Any]          =       ids_tensor([self.batch_size, self.seq_length]				, self.num_labels      )
											UpperCAmelCase_      :       int          =       ids_tensor([self.batch_size]				, self.num_choices      )

								UpperCAmelCase_      :       int          =       LayoutLMConfig(
								    vocab_size=self.vocab_size				, hidden_size=self.hidden_size				, num_hidden_layers=self.num_hidden_layers				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, hidden_act=self.hidden_act				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, max_position_embeddings=self.max_position_embeddings				, type_vocab_size=self.type_vocab_size				, initializer_range=self.initializer_range				, )

								return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




					def 		A      (  self  :		int				, _A  :		Optional[Any]				, _A  :		str				, _A  :		Tuple				, _A  :		int				, _A  :		Optional[int]				, _A  :		int				, _A  :		int				, _A  :		Tuple      )    ->							Tuple:
								UpperCAmelCase_      :       Optional[int]          =       TFLayoutLMModel(config=_A      )

								UpperCAmelCase_      :       List[Any]          =       model(_A				, _A				, attention_mask=_A				, token_type_ids=_A      )
								UpperCAmelCase_      :       int          =       model(_A				, _A				, token_type_ids=_A      )
								UpperCAmelCase_      :       List[str]          =       model(_A				, _A      )
								self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)      )
								self.parent.assertEqual(result.pooler_output.shape				, (self.batch_size, self.hidden_size)      )




					def 		A      (  self  :		Dict				, _A  :		List[Any]				, _A  :		Any				, _A  :		Union[str, Any]				, _A  :		List[str]				, _A  :		Any				, _A  :		List[str]				, _A  :		Any				, _A  :		List[str]      )    ->							Tuple:
								UpperCAmelCase_      :       Tuple          =       TFLayoutLMForMaskedLM(config=_A      )

								UpperCAmelCase_      :       Optional[int]          =       model(_A				, _A				, attention_mask=_A				, token_type_ids=_A				, labels=_A      )
								self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.vocab_size)      )




					def 		A      (  self  :		Tuple				, _A  :		Optional[int]				, _A  :		Union[str, Any]				, _A  :		Dict				, _A  :		List[Any]				, _A  :		str				, _A  :		List[str]				, _A  :		str				, _A  :		Optional[Any]      )    ->							List[str]:
								UpperCAmelCase_      :       int          =       self.num_labels
								UpperCAmelCase_      :       str          =       TFLayoutLMForSequenceClassification(config=_A      )

								UpperCAmelCase_      :       Union[str, Any]          =       model(_A				, _A				, attention_mask=_A				, token_type_ids=_A      )
								self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)      )




					def 		A      (  self  :		int				, _A  :		Union[str, Any]				, _A  :		Tuple				, _A  :		str				, _A  :		List[Any]				, _A  :		int				, _A  :		List[str]				, _A  :		Tuple				, _A  :		Union[str, Any]      )    ->							List[Any]:
								UpperCAmelCase_      :       Dict          =       self.num_labels
								UpperCAmelCase_      :       str          =       TFLayoutLMForTokenClassification(config=_A      )

								UpperCAmelCase_      :       Any          =       model(_A				, _A				, attention_mask=_A				, token_type_ids=_A				, labels=_A      )
								self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.num_labels)      )




					def 		A      (  self  :		List[str]				, _A  :		int				, _A  :		Union[str, Any]				, _A  :		int				, _A  :		List[str]				, _A  :		Optional[int]				, _A  :		Any				, _A  :		Any				, _A  :		Union[str, Any]      )    ->							Dict:
								UpperCAmelCase_      :       Any          =       TFLayoutLMForQuestionAnswering(config=_A      )

								UpperCAmelCase_      :       Union[str, Any]          =       model(_A				, _A				, attention_mask=_A				, token_type_ids=_A      )
								self.parent.assertEqual(result.start_logits.shape				, (self.batch_size, self.seq_length)      )
								self.parent.assertEqual(result.end_logits.shape				, (self.batch_size, self.seq_length)      )





					def 		A      (  self  :		int      )    ->							Tuple:
								UpperCAmelCase_      :       Optional[int]          =       self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      (
								    UpperCAmelCase_
								)    ,      
								)      :       Optional[int]          =       config_and_inputs
								UpperCAmelCase_      :       str          =       {
								    '''input_ids''': input_ids,
								    '''bbox''': bbox,
								    '''token_type_ids''': token_type_ids,
								    '''attention_mask''': input_mask,
								}
								return config, inputs_dict






@require_tf
class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					(
					    (
					        TFLayoutLMModel,
					        TFLayoutLMForMaskedLM,
					        TFLayoutLMForTokenClassification,
					        TFLayoutLMForSequenceClassification,
					        TFLayoutLMForQuestionAnswering,
					    )
					    if is_tf_available()
					    else ()
					)
					a_        =					(
					    {
					        "feature-extraction": TFLayoutLMModel,
					        "fill-mask": TFLayoutLMForMaskedLM,
					        "text-classification": TFLayoutLMForSequenceClassification,
					        "token-classification": TFLayoutLMForTokenClassification,
					        "zero-shot": TFLayoutLMForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					a_        =					False
					a_        =					True
					a_        =					10




					def 		A      (  self  :		List[str]      )    ->							List[Any]:
								UpperCAmelCase_      :       Tuple          =       TFLayoutLMModelTester(self      )
								UpperCAmelCase_      :       Union[str, Any]          =       ConfigTester(self				, config_class=_A				, hidden_size=37      )




					def 		A      (  self  :		List[str]      )    ->							Dict:
								self.config_tester.run_common_tests()




					def 		A      (  self  :		Union[str, Any]      )    ->							Dict:
								UpperCAmelCase_      :       Optional[int]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_A      )




					def 		A      (  self  :		List[Any]      )    ->							List[Any]:
								UpperCAmelCase_      :       Union[str, Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*_A      )




					def 		A      (  self  :		str      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       List[Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*_A      )




					def 		A      (  self  :		Dict      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Union[str, Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*_A      )




					def 		A      (  self  :		Union[str, Any]      )    ->							List[Any]:
								UpperCAmelCase_      :       Dict          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*_A      )




					@slow
					def 		A      (  self  :		str      )    ->							Dict:
								for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCAmelCase_      :       Optional[Any]          =       TFLayoutLMModel.from_pretrained(_A      )
											self.assertIsNotNone(_A      )





					@unittest.skip('''Onnx compliancy broke with TF 2.10'''      )
					def 		A      (  self  :		Tuple      )    ->							int:
								pass






def __UpperCAmelCase   (					)    ->   str:
			# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
			# fmt: off
			UpperCAmelCase_      :       str          =       tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]		)  # noqa: E231
			UpperCAmelCase_      :       int          =       tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]		)  # noqa: E231
			UpperCAmelCase_      :       Optional[int]          =       tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]		)  # noqa: E231
			UpperCAmelCase_      :       int          =       tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]		)  # noqa: E231
			# these are sequence labels (i.e. at the token level)
			UpperCAmelCase_      :       Tuple          =       tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]		)  # noqa: E231
			# fmt: on

			return input_ids, attention_mask, bbox, token_type_ids, labels







@require_tf
class  snake_case__      (					unittest.TestCase):




					@slow
					def 		A      (  self  :		Any      )    ->							Optional[Any]:
								UpperCAmelCase_      :       str          =       TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       prepare_layoutlm_batch_inputs()

								# forward pass
								UpperCAmelCase_      :       Optional[Any]          =       model(input_ids=_A				, bbox=_A				, attention_mask=_A				, token_type_ids=_A      )

								# test the sequence output on [0, :3, :3]
								UpperCAmelCase_      :       Any          =       tf.convert_to_tensor(
								    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]				, )

								self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]				, _A				, atol=1e-3      )      )

								# test the pooled output on [1, :3]
								UpperCAmelCase_      :       Optional[Any]          =       tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]      )

								self.assertTrue(np.allclose(outputs.pooler_output[1, :3]				, _A				, atol=1e-3      )      )




					@slow
					def 		A      (  self  :		int      )    ->							Any:
								# initialize model with randomly initialized sequence classification head
								UpperCAmelCase_      :       str          =       TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''				, num_labels=2      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Union[str, Any]          =       prepare_layoutlm_batch_inputs()

								# forward pass
								UpperCAmelCase_      :       List[Any]          =       model(
								    input_ids=_A				, bbox=_A				, attention_mask=_A				, token_type_ids=_A				, labels=tf.convert_to_tensor([1, 1]      )				, )

								# test whether we get a loss as a scalar
								UpperCAmelCase_      :       Dict          =       outputs.loss
								UpperCAmelCase_      :       str          =       (2,)
								self.assertEqual(loss.shape				, _A      )

								# test the shape of the logits
								UpperCAmelCase_      :       Optional[Any]          =       outputs.logits
								UpperCAmelCase_      :       str          =       (2, 2)
								self.assertEqual(logits.shape				, _A      )




					@slow
					def 		A      (  self  :		int      )    ->							int:
								# initialize model with randomly initialized token classification head
								UpperCAmelCase_      :       Tuple          =       TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''				, num_labels=13      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       prepare_layoutlm_batch_inputs()

								# forward pass
								UpperCAmelCase_      :       Optional[Any]          =       model(
								    input_ids=_A				, bbox=_A				, attention_mask=_A				, token_type_ids=_A				, labels=_A      )

								# test the shape of the logits
								UpperCAmelCase_      :       int          =       outputs.logits
								UpperCAmelCase_      :       List[str]          =       tf.convert_to_tensor((2, 25, 13)      )
								self.assertEqual(logits.shape				, _A      )





					@slow
					def 		A      (  self  :		Tuple      )    ->							str:
								# initialize model with randomly initialized token classification head
								UpperCAmelCase_      :       int          =       TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       prepare_layoutlm_batch_inputs()

								# forward pass
								UpperCAmelCase_      :       str          =       model(input_ids=_A				, bbox=_A				, attention_mask=_A				, token_type_ids=_A      )

								# test the shape of the logits
								UpperCAmelCase_      :       List[Any]          =       tf.convert_to_tensor((2, 25)      )
								self.assertEqual(outputs.start_logits.shape				, _A      )
								self.assertEqual(outputs.end_logits.shape				, _A      )










'''simple docstring'''





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_UpperCamelCase						:						str  =					{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class  snake_case__      (					nn.Module):




					def __init__(  self  :		Optional[int]				, _A  :		Tuple      )    ->							List[str]:
								super().__init__()
								UpperCAmelCase_      :       Tuple          =       torchvision.models.resnetaaa(pretrained=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       list(model.children()      )[:-2]
								UpperCAmelCase_      :       Union[str, Any]          =       nn.Sequential(*_A      )
								UpperCAmelCase_      :       List[Any]          =       nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]      )





					def 		A      (  self  :		str				, _A  :		Optional[int]      )    ->							str:
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								UpperCAmelCase_      :       List[str]          =       self.pool(self.model(_A      )      )
								UpperCAmelCase_      :       Tuple          =       torch.flatten(_A				, start_dim=2      )
								UpperCAmelCase_      :       Union[str, Any]          =       out.transpose(1				, 2      ).contiguous()
								return out  # BxNx2048






class  snake_case__      (					UpperCamelCase):




					def __init__(  self  :		Optional[int]				, _A  :		int				, _A  :		str				, _A  :		int				, _A  :		Dict				, _A  :		int      )    ->							List[str]:
								UpperCAmelCase_      :       Any          =       [json.loads(_A      ) for l in open(_A      )]
								UpperCAmelCase_      :       Tuple          =       os.path.dirname(_A      )
								UpperCAmelCase_      :       Any          =       tokenizer
								UpperCAmelCase_      :       Optional[Any]          =       labels
								UpperCAmelCase_      :       List[str]          =       len(_A      )
								UpperCAmelCase_      :       int          =       max_seq_length

								UpperCAmelCase_      :       str          =       transforms




					def __len__(  self  :		Tuple      )    ->							Tuple:
								return len(self.data      )




					def __getitem__(  self  :		Union[str, Any]				, _A  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       Tuple          =       torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				, add_special_tokens=_A      )      )
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       sentence[0], sentence[1:-1], sentence[-1]
								UpperCAmelCase_      :       List[str]          =       sentence[: self.max_seq_length]

								UpperCAmelCase_      :       List[str]          =       torch.zeros(self.n_classes      )
								UpperCAmelCase_      :       Optional[int]          =       1

								UpperCAmelCase_      :       Tuple          =       Image.open(os.path.join(self.data_dir				, self.data[index]['''img''']      )      ).convert('''RGB'''      )
								UpperCAmelCase_      :       List[Any]          =       self.transforms(_A      )

								return {
								    "image_start_token": start_token,
								    "image_end_token": end_token,
								    "sentence": sentence,
								    "image": image,
								    "label": label,
								}





					def 		A      (  self  :		Tuple      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Tuple          =       Counter()
								for row in self.data:
											label_freqs.update(row['''label''']      )
								return label_freqs






def __UpperCAmelCase   (					A					:			Tuple		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       int          =       [len(row['''sentence''']		) for row in batch]
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       len(A		), max(A		)

			UpperCAmelCase_      :       Tuple          =       torch.zeros(A			,       A			,       dtype=torch.long		)
			UpperCAmelCase_      :       Dict          =       torch.zeros(A			,       A			,       dtype=torch.long		)

			for i_batch, (input_row, length) in enumerate(zip(A			,       A		)		):
						UpperCAmelCase_      :       int          =       input_row['''sentence''']
						UpperCAmelCase_      :       Optional[Any]          =       1

			UpperCAmelCase_      :       Union[str, Any]          =       torch.stack([row['''image'''] for row in batch]		)
			UpperCAmelCase_      :       Optional[int]          =       torch.stack([row['''label'''] for row in batch]		)
			UpperCAmelCase_      :       Any          =       torch.stack([row['''image_start_token'''] for row in batch]		)
			UpperCAmelCase_      :       int          =       torch.stack([row['''image_end_token'''] for row in batch]		)

			return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return [
			    "Crime",
			    "Drama",
			    "Thriller",
			    "Action",
			    "Comedy",
			    "Romance",
			    "Documentary",
			    "Short",
			    "Mystery",
			    "History",
			    "Family",
			    "Adventure",
			    "Fantasy",
			    "Sci-Fi",
			    "Western",
			    "Horror",
			    "Sport",
			    "War",
			    "Music",
			    "Musical",
			    "Animation",
			    "Biography",
			    "Film-Noir",
			]







def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return transforms.Compose(
			    [
			        transforms.Resize(2_5_6		),
			        transforms.CenterCrop(2_2_4		),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.46777044, 0.44531429, 0.40661017]			,       std=[0.12221994, 0.12145835, 0.14380469]			,       ),
			    ]		)


